Overview

Dataset statistics

Number of variables37
Number of observations234
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory67.8 KiB
Average record size in memory296.5 B

Variable types

Categorical27
Numeric10

Alerts

Job Title has a high cardinality: 127 distinct values High cardinality
Founded is highly correlated with Size and 2 other fieldsHigh correlation
Lower Salary is highly correlated with Rating and 5 other fieldsHigh correlation
Upper Salary is highly correlated with Industry and 5 other fieldsHigh correlation
Avg Salary(K) is highly correlated with Rating and 7 other fieldsHigh correlation
Age is highly correlated with Founded and 4 other fieldsHigh correlation
spark is highly correlated with Python and 1 other fieldsHigh correlation
pytorch is highly correlated with scikit and 1 other fieldsHigh correlation
tensor is highly correlated with keras and 2 other fieldsHigh correlation
hadoop is highly correlated with sparkHigh correlation
Min_Salary is highly correlated with Hourly and 6 other fieldsHigh correlation
Max_Salary is highly correlated with Industry and 7 other fieldsHigh correlation
Min_Revenue is highly correlated with Size and 4 other fieldsHigh correlation
Max_Revenue is highly correlated with Size and 4 other fieldsHigh correlation
Type of ownership is highly correlated with Industry and 6 other fieldsHigh correlation
job_title_sim is highly correlated with Industry and 5 other fieldsHigh correlation
Python is highly correlated with Industry and 2 other fieldsHigh correlation
Hourly is highly correlated with Type of ownership and 3 other fieldsHigh correlation
Industry is highly correlated with Rating and 14 other fieldsHigh correlation
Sector is highly correlated with Size and 8 other fieldsHigh correlation
google_an is highly correlated with Industry and 1 other fieldsHigh correlation
Job Location is highly correlated with Size and 8 other fieldsHigh correlation
sql is highly correlated with Industry and 2 other fieldsHigh correlation
Rating is highly correlated with Industry and 3 other fieldsHigh correlation
Size is highly correlated with Founded and 5 other fieldsHigh correlation
Employer provided is highly correlated with Rating and 5 other fieldsHigh correlation
keras is highly correlated with tensorHigh correlation
scikit is highly correlated with pytorch and 1 other fieldsHigh correlation
tableau is highly correlated with biHigh correlation
bi is highly correlated with tableauHigh correlation
seniority_by_title is highly correlated with Avg Salary(K)High correlation
Degree is highly correlated with job_title_simHigh correlation

Reproduction

Analysis started2022-10-02 04:34:42.398811
Analysis finished2022-10-02 04:35:22.395935
Duration40 seconds
Software versionpandas-profiling v3.3.0
Download configurationconfig.json

Variables

Job Title
Categorical

HIGH CARDINALITY

Distinct127
Distinct (%)54.3%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
Data Scientist
51 
Data Engineer
23 
Senior Data Scientist
 
11
Data Analyst
 
10
Senior Data Engineer
 
7
Other values (122)
132 

Length

Max length98
Median length65
Mean length24.63675214
Min length9

Characters and Unicode

Total characters5765
Distinct characters62
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique116 ?
Unique (%)49.6%

Sample

1st rowData Scientist
2nd rowHealthcare Data Scientist
3rd rowData Scientist
4th rowData Scientist
5th rowData Scientist

Common Values

ValueCountFrequency (%)
Data Scientist51
21.8%
Data Engineer23
 
9.8%
Senior Data Scientist11
 
4.7%
Data Analyst10
 
4.3%
Senior Data Engineer7
 
3.0%
Senior Data Analyst5
 
2.1%
Scientist3
 
1.3%
Clinical Data Analyst2
 
0.9%
Research Scientist2
 
0.9%
Sr. Data Engineer2
 
0.9%
Other values (117)118
50.4%

Length

2022-10-02T10:05:22.729388image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
data193
24.2%
scientist127
15.9%
engineer52
 
6.5%
analyst42
 
5.3%
senior39
 
4.9%
38
 
4.8%
sr11
 
1.4%
analytics11
 
1.4%
science9
 
1.1%
research8
 
1.0%
Other values (168)268
33.6%

Most occurring characters

ValueCountFrequency (%)
t647
11.2%
a633
11.0%
564
 
9.8%
i530
 
9.2%
e485
 
8.4%
n474
 
8.2%
s276
 
4.8%
c254
 
4.4%
S229
 
4.0%
r225
 
3.9%
Other values (52)1448
25.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter4261
73.9%
Uppercase Letter813
 
14.1%
Space Separator564
 
9.8%
Other Punctuation58
 
1.0%
Dash Punctuation35
 
0.6%
Decimal Number12
 
0.2%
Open Punctuation11
 
0.2%
Close Punctuation11
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t647
15.2%
a633
14.9%
i530
12.4%
e485
11.4%
n474
11.1%
s276
6.5%
c254
 
6.0%
r225
 
5.3%
l155
 
3.6%
o144
 
3.4%
Other values (15)438
10.3%
Uppercase Letter
ValueCountFrequency (%)
S229
28.2%
D206
25.3%
A85
 
10.5%
E62
 
7.6%
C33
 
4.1%
M32
 
3.9%
I28
 
3.4%
P26
 
3.2%
L22
 
2.7%
R16
 
2.0%
Other values (13)74
 
9.1%
Other Punctuation
ValueCountFrequency (%)
,25
43.1%
/17
29.3%
.8
 
13.8%
&7
 
12.1%
:1
 
1.7%
Decimal Number
ValueCountFrequency (%)
25
41.7%
05
41.7%
41
 
8.3%
11
 
8.3%
Dash Punctuation
ValueCountFrequency (%)
-30
85.7%
5
 
14.3%
Space Separator
ValueCountFrequency (%)
564
100.0%
Open Punctuation
ValueCountFrequency (%)
(11
100.0%
Close Punctuation
ValueCountFrequency (%)
)11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin5074
88.0%
Common691
 
12.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
t647
12.8%
a633
12.5%
i530
10.4%
e485
9.6%
n474
9.3%
s276
 
5.4%
c254
 
5.0%
S229
 
4.5%
r225
 
4.4%
D206
 
4.1%
Other values (38)1115
22.0%
Common
ValueCountFrequency (%)
564
81.6%
-30
 
4.3%
,25
 
3.6%
/17
 
2.5%
(11
 
1.6%
)11
 
1.6%
.8
 
1.2%
&7
 
1.0%
25
 
0.7%
05
 
0.7%
Other values (4)8
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII5760
99.9%
Punctuation5
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t647
11.2%
a633
11.0%
564
 
9.8%
i530
 
9.2%
e485
 
8.4%
n474
 
8.2%
s276
 
4.8%
c254
 
4.4%
S229
 
4.0%
r225
 
3.9%
Other values (51)1443
25.1%
Punctuation
ValueCountFrequency (%)
5
100.0%

Rating
Real number (ℝ)

HIGH CORRELATION

Distinct30
Distinct (%)12.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.714529915
Minimum-1
Maximum5
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)0.4%
Memory size2.0 KiB
2022-10-02T10:05:23.010651image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile2.7
Q13.4
median3.8
Q34.1
95-th percentile4.7
Maximum5
Range6
Interquartile range (IQR)0.7

Descriptive statistics

Standard deviation0.6714617911
Coefficient of variation (CV)0.1807662898
Kurtosis9.790065755
Mean3.714529915
Median Absolute Deviation (MAD)0.4
Skewness-1.760643289
Sum869.2
Variance0.4508609369
MonotonicityNot monotonic
2022-10-02T10:05:23.258894image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
3.829
 
12.4%
3.923
 
9.8%
3.418
 
7.7%
3.517
 
7.3%
417
 
7.3%
4.714
 
6.0%
3.210
 
4.3%
3.310
 
4.3%
4.310
 
4.3%
3.710
 
4.3%
Other values (20)76
32.5%
ValueCountFrequency (%)
-11
 
0.4%
1.92
 
0.9%
2.12
 
0.9%
2.21
 
0.4%
2.32
 
0.9%
2.42
 
0.9%
2.51
 
0.4%
2.75
2.1%
2.84
1.7%
2.92
 
0.9%
ValueCountFrequency (%)
51
 
0.4%
4.83
 
1.3%
4.714
6.0%
4.67
3.0%
4.54
 
1.7%
4.48
3.4%
4.310
4.3%
4.26
 
2.6%
4.17
3.0%
417
7.3%

Size
Categorical

HIGH CORRELATION

Distinct7
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
1001 - 5000
55 
201 - 500
44 
501 - 1000
40 
51 - 200
38 
10000+
29 
Other values (2)
28 

Length

Max length13
Median length11
Mean length10.18376068
Min length7

Characters and Unicode

Total characters2383
Distinct characters7
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row501 - 1000
2nd row10000+
3rd row501 - 1000
4th row1001 - 5000
5th row201 - 500

Common Values

ValueCountFrequency (%)
1001 - 5000 55
23.5%
201 - 500 44
18.8%
501 - 1000 40
17.1%
51 - 200 38
16.2%
10000+ 29
12.4%
5001 - 10000 17
 
7.3%
1 - 50 11
 
4.7%

Length

2022-10-02T10:05:23.536028image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-02T10:05:23.796352image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
205
31.8%
100155
 
8.5%
500055
 
8.5%
1000046
 
7.1%
20144
 
6.8%
50044
 
6.8%
50140
 
6.2%
100040
 
6.2%
5138
 
5.9%
20038
 
5.9%
Other values (3)39
 
6.1%

Most occurring characters

ValueCountFrequency (%)
0872
36.6%
644
27.0%
1346
 
14.5%
-205
 
8.6%
5205
 
8.6%
282
 
3.4%
+29
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1505
63.2%
Space Separator644
27.0%
Dash Punctuation205
 
8.6%
Math Symbol29
 
1.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0872
57.9%
1346
 
23.0%
5205
 
13.6%
282
 
5.4%
Space Separator
ValueCountFrequency (%)
644
100.0%
Dash Punctuation
ValueCountFrequency (%)
-205
100.0%
Math Symbol
ValueCountFrequency (%)
+29
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common2383
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0872
36.6%
644
27.0%
1346
 
14.5%
-205
 
8.6%
5205
 
8.6%
282
 
3.4%
+29
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII2383
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0872
36.6%
644
27.0%
1346
 
14.5%
-205
 
8.6%
5205
 
8.6%
282
 
3.4%
+29
 
1.2%

Founded
Real number (ℝ)

HIGH CORRELATION

Distinct54
Distinct (%)23.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1840.799145
Minimum-1
Maximum2019
Zeros0
Zeros (%)0.0%
Negative18
Negative (%)7.7%
Memory size2.0 KiB
2022-10-02T10:05:24.111128image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q11977
median1996
Q32006
95-th percentile2014
Maximum2019
Range2020
Interquartile range (IQR)29

Descriptive statistics

Standard deviation533.032178
Coefficient of variation (CV)0.2895656375
Kurtosis8.267319491
Mean1840.799145
Median Absolute Deviation (MAD)12
Skewness-3.191483543
Sum430747
Variance284123.3028
MonotonicityNot monotonic
2022-10-02T10:05:24.419445image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-118
 
7.7%
199615
 
6.4%
201013
 
5.6%
200613
 
5.6%
200812
 
5.1%
20029
 
3.8%
19977
 
3.0%
19657
 
3.0%
20007
 
3.0%
19826
 
2.6%
Other values (44)127
54.3%
ValueCountFrequency (%)
-118
7.7%
19614
 
1.7%
19621
 
0.4%
19657
 
3.0%
19663
 
1.3%
19672
 
0.9%
19685
 
2.1%
19695
 
2.1%
19702
 
0.9%
19711
 
0.4%
ValueCountFrequency (%)
20191
 
0.4%
20174
 
1.7%
20162
 
0.9%
20151
 
0.4%
20145
 
2.1%
20133
 
1.3%
20125
 
2.1%
20115
 
2.1%
201013
5.6%
20093
 
1.3%

Type of ownership
Categorical

HIGH CORRELATION

Distinct8
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
Company - Private
134 
Company - Public
62 
Subsidiary or Business Segment
15 
Government
 
7
Nonprofit Organization
 
6
Other values (3)
 
10

Length

Max length30
Median length17
Mean length17.36752137
Min length8

Characters and Unicode

Total characters4064
Distinct characters33
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCompany - Private
2nd rowOther Organization
3rd rowCompany - Private
4th rowGovernment
5th rowCompany - Public

Common Values

ValueCountFrequency (%)
Company - Private134
57.3%
Company - Public62
26.5%
Subsidiary or Business Segment15
 
6.4%
Government7
 
3.0%
Nonprofit Organization6
 
2.6%
Hospital5
 
2.1%
Other Organization3
 
1.3%
School / School District2
 
0.9%

Length

2022-10-02T10:05:24.713298image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-02T10:05:24.983610image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
198
28.9%
company196
28.6%
private134
19.5%
public62
 
9.0%
subsidiary15
 
2.2%
or15
 
2.2%
business15
 
2.2%
segment15
 
2.2%
organization9
 
1.3%
government7
 
1.0%
Other values (5)20
 
2.9%

Most occurring characters

ValueCountFrequency (%)
452
 
11.1%
a368
 
9.1%
i274
 
6.7%
n264
 
6.5%
o252
 
6.2%
m218
 
5.4%
y211
 
5.2%
p207
 
5.1%
C196
 
4.8%
-196
 
4.8%
Other values (23)1426
35.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2941
72.4%
Uppercase Letter473
 
11.6%
Space Separator452
 
11.1%
Dash Punctuation196
 
4.8%
Other Punctuation2
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a368
12.5%
i274
9.3%
n264
9.0%
o252
8.6%
m218
 
7.4%
y211
 
7.2%
p207
 
7.0%
e196
 
6.7%
r191
 
6.5%
t183
 
6.2%
Other values (11)577
19.6%
Uppercase Letter
ValueCountFrequency (%)
C196
41.4%
P196
41.4%
S34
 
7.2%
B15
 
3.2%
O12
 
2.5%
G7
 
1.5%
N6
 
1.3%
H5
 
1.1%
D2
 
0.4%
Space Separator
ValueCountFrequency (%)
452
100.0%
Dash Punctuation
ValueCountFrequency (%)
-196
100.0%
Other Punctuation
ValueCountFrequency (%)
/2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin3414
84.0%
Common650
 
16.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a368
 
10.8%
i274
 
8.0%
n264
 
7.7%
o252
 
7.4%
m218
 
6.4%
y211
 
6.2%
p207
 
6.1%
C196
 
5.7%
P196
 
5.7%
e196
 
5.7%
Other values (20)1032
30.2%
Common
ValueCountFrequency (%)
452
69.5%
-196
30.2%
/2
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII4064
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
452
 
11.1%
a368
 
9.1%
i274
 
6.7%
n264
 
6.5%
o252
 
6.2%
m218
 
5.4%
y211
 
5.2%
p207
 
5.1%
C196
 
4.8%
-196
 
4.8%
Other values (23)1426
35.1%

Industry
Categorical

HIGH CORRELATION

Distinct43
Distinct (%)18.4%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
Biotech & Pharmaceuticals
29 
IT Services
24 
Computer Hardware & Software
23 
Consulting
15 
Enterprise Software & Network Solutions
 
13
Other values (38)
130 

Length

Max length39
Median length32
Mean length20.81623932
Min length2

Characters and Unicode

Total characters4871
Distinct characters51
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique17 ?
Unique (%)7.3%

Sample

1st rowAerospace & Defense
2nd rowHealth Care Services & Hospitals
3rd rowSecurity Services
4th rowEnergy
5th rowReal Estate

Common Values

ValueCountFrequency (%)
Biotech & Pharmaceuticals29
 
12.4%
IT Services24
 
10.3%
Computer Hardware & Software23
 
9.8%
Consulting15
 
6.4%
Enterprise Software & Network Solutions13
 
5.6%
Aerospace & Defense12
 
5.1%
Insurance Carriers12
 
5.1%
Health Care Services & Hospitals11
 
4.7%
Internet10
 
4.3%
Energy8
 
3.4%
Other values (33)77
32.9%

Length

2022-10-02T10:05:25.307506image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
124
19.1%
services43
 
6.6%
software36
 
5.6%
biotech29
 
4.5%
pharmaceuticals29
 
4.5%
it24
 
3.7%
computer23
 
3.5%
hardware23
 
3.5%
consulting15
 
2.3%
enterprise13
 
2.0%
Other values (70)289
44.6%

Most occurring characters

ValueCountFrequency (%)
e558
 
11.5%
414
 
8.5%
r392
 
8.0%
a350
 
7.2%
t323
 
6.6%
i276
 
5.7%
s266
 
5.5%
n253
 
5.2%
c227
 
4.7%
o226
 
4.6%
Other values (41)1586
32.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter3769
77.4%
Uppercase Letter548
 
11.3%
Space Separator414
 
8.5%
Other Punctuation132
 
2.7%
Decimal Number5
 
0.1%
Dash Punctuation3
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e558
14.8%
r392
10.4%
a350
9.3%
t323
8.6%
i276
 
7.3%
s266
 
7.1%
n253
 
6.7%
c227
 
6.0%
o226
 
6.0%
u142
 
3.8%
Other values (14)756
20.1%
Uppercase Letter
ValueCountFrequency (%)
S111
20.3%
C70
12.8%
I48
8.8%
H46
8.4%
T36
 
6.6%
A35
 
6.4%
P35
 
6.4%
B34
 
6.2%
E31
 
5.7%
D23
 
4.2%
Other values (11)79
14.4%
Other Punctuation
ValueCountFrequency (%)
&124
93.9%
,8
 
6.1%
Decimal Number
ValueCountFrequency (%)
13
60.0%
22
40.0%
Space Separator
ValueCountFrequency (%)
414
100.0%
Dash Punctuation
ValueCountFrequency (%)
-3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin4317
88.6%
Common554
 
11.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e558
12.9%
r392
 
9.1%
a350
 
8.1%
t323
 
7.5%
i276
 
6.4%
s266
 
6.2%
n253
 
5.9%
c227
 
5.3%
o226
 
5.2%
u142
 
3.3%
Other values (35)1304
30.2%
Common
ValueCountFrequency (%)
414
74.7%
&124
 
22.4%
,8
 
1.4%
-3
 
0.5%
13
 
0.5%
22
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII4871
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e558
 
11.5%
414
 
8.5%
r392
 
8.0%
a350
 
7.2%
t323
 
6.6%
i276
 
5.7%
s266
 
5.5%
n253
 
5.2%
c227
 
4.7%
o226
 
4.6%
Other values (41)1586
32.6%

Sector
Categorical

HIGH CORRELATION

Distinct22
Distinct (%)9.4%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
Information Technology
70 
Business Services
43 
Biotech & Pharmaceuticals
29 
Finance
13 
Aerospace & Defense
12 
Other values (17)
67 

Length

Max length32
Median length26
Mean length18
Min length2

Characters and Unicode

Total characters4212
Distinct characters42
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)2.6%

Sample

1st rowAerospace & Defense
2nd rowHealth Care
3rd rowBusiness Services
4th rowOil, Gas, Energy & Utilities
5th rowReal Estate

Common Values

ValueCountFrequency (%)
Information Technology70
29.9%
Business Services43
18.4%
Biotech & Pharmaceuticals29
12.4%
Finance13
 
5.6%
Aerospace & Defense12
 
5.1%
Insurance12
 
5.1%
Health Care11
 
4.7%
Oil, Gas, Energy & Utilities8
 
3.4%
Manufacturing7
 
3.0%
Retail4
 
1.7%
Other values (12)25
 
10.7%

Length

2022-10-02T10:05:25.595461image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
information70
14.2%
technology70
14.2%
56
11.4%
services44
 
8.9%
business43
 
8.7%
biotech29
 
5.9%
pharmaceuticals29
 
5.9%
finance13
 
2.6%
defense12
 
2.4%
insurance12
 
2.4%
Other values (27)114
23.2%

Most occurring characters

ValueCountFrequency (%)
e415
 
9.9%
n359
 
8.5%
o345
 
8.2%
i295
 
7.0%
s270
 
6.4%
a268
 
6.4%
258
 
6.1%
c258
 
6.1%
r210
 
5.0%
t192
 
4.6%
Other values (32)1342
31.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter3440
81.7%
Uppercase Letter437
 
10.4%
Space Separator258
 
6.1%
Other Punctuation73
 
1.7%
Dash Punctuation3
 
0.1%
Decimal Number1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e415
12.1%
n359
10.4%
o345
10.0%
i295
8.6%
s270
 
7.8%
a268
 
7.8%
c258
 
7.5%
r210
 
6.1%
t192
 
5.6%
l142
 
4.1%
Other values (9)686
19.9%
Uppercase Letter
ValueCountFrequency (%)
I82
18.8%
T82
18.8%
B72
16.5%
S44
10.1%
P31
 
7.1%
E16
 
3.7%
A14
 
3.2%
F13
 
3.0%
D12
 
2.7%
C12
 
2.7%
Other values (8)59
13.5%
Other Punctuation
ValueCountFrequency (%)
&56
76.7%
,17
 
23.3%
Space Separator
ValueCountFrequency (%)
258
100.0%
Dash Punctuation
ValueCountFrequency (%)
-3
100.0%
Decimal Number
ValueCountFrequency (%)
11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin3877
92.0%
Common335
 
8.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e415
 
10.7%
n359
 
9.3%
o345
 
8.9%
i295
 
7.6%
s270
 
7.0%
a268
 
6.9%
c258
 
6.7%
r210
 
5.4%
t192
 
5.0%
l142
 
3.7%
Other values (27)1123
29.0%
Common
ValueCountFrequency (%)
258
77.0%
&56
 
16.7%
,17
 
5.1%
-3
 
0.9%
11
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII4212
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e415
 
9.9%
n359
 
8.5%
o345
 
8.2%
i295
 
7.0%
s270
 
6.4%
a268
 
6.4%
258
 
6.1%
c258
 
6.1%
r210
 
5.0%
t192
 
4.6%
Other values (32)1342
31.9%

Hourly
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
0
229 
1
 
5

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters234
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0229
97.9%
15
 
2.1%

Length

2022-10-02T10:05:25.827832image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-02T10:05:26.074414image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0229
97.9%
15
 
2.1%

Most occurring characters

ValueCountFrequency (%)
0229
97.9%
15
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number234
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0229
97.9%
15
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
Common234
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0229
97.9%
15
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII234
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0229
97.9%
15
 
2.1%

Employer provided
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
0
233 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters234
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.4%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0233
99.6%
11
 
0.4%

Length

2022-10-02T10:05:26.382590image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-02T10:05:26.617449image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0233
99.6%
11
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0233
99.6%
11
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number234
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0233
99.6%
11
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Common234
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0233
99.6%
11
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII234
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0233
99.6%
11
 
0.4%

Lower Salary
Real number (ℝ≥0)

HIGH CORRELATION

Distinct90
Distinct (%)38.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean71.76068376
Minimum15
Maximum200
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2022-10-02T10:05:26.846307image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile32.65
Q152
median68
Q386
95-th percentile118.7
Maximum200
Range185
Interquartile range (IQR)34

Descriptive statistics

Standard deviation28.24893923
Coefficient of variation (CV)0.3936548226
Kurtosis2.04284193
Mean71.76068376
Median Absolute Deviation (MAD)18
Skewness0.9554970909
Sum16792
Variance798.0025678
MonotonicityNot monotonic
2022-10-02T10:05:27.165907image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
527
 
3.0%
646
 
2.6%
836
 
2.6%
606
 
2.6%
746
 
2.6%
806
 
2.6%
865
 
2.1%
555
 
2.1%
655
 
2.1%
425
 
2.1%
Other values (80)177
75.6%
ValueCountFrequency (%)
151
 
0.4%
202
0.9%
271
 
0.4%
291
 
0.4%
314
1.7%
323
1.3%
332
0.9%
341
 
0.4%
351
 
0.4%
361
 
0.4%
ValueCountFrequency (%)
2001
0.4%
1901
0.4%
1391
0.4%
1381
0.4%
1321
0.4%
1311
0.4%
1301
0.4%
1271
0.4%
1262
0.9%
1211
0.4%

Upper Salary
Real number (ℝ≥0)

HIGH CORRELATION

Distinct122
Distinct (%)52.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean124.5512821
Minimum16
Maximum250
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2022-10-02T10:05:27.490395image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum16
5-th percentile61.65
Q195.25
median120.5
Q3148
95-th percentile207.35
Maximum250
Range234
Interquartile range (IQR)52.75

Descriptive statistics

Standard deviation42.73161501
Coefficient of variation (CV)0.3430845055
Kurtosis0.05296412477
Mean124.5512821
Median Absolute Deviation (MAD)27
Skewness0.428129645
Sum29145
Variance1825.990921
MonotonicityNot monotonic
2022-10-02T10:05:27.785936image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1137
 
3.0%
1335
 
2.1%
855
 
2.1%
1375
 
2.1%
1275
 
2.1%
1195
 
2.1%
1424
 
1.7%
1264
 
1.7%
1404
 
1.7%
1124
 
1.7%
Other values (112)186
79.5%
ValueCountFrequency (%)
161
0.4%
351
0.4%
391
0.4%
481
0.4%
501
0.4%
521
0.4%
551
0.4%
572
0.9%
591
0.4%
601
0.4%
ValueCountFrequency (%)
2501
0.4%
2311
0.4%
2282
0.9%
2241
0.4%
2231
0.4%
2211
0.4%
2202
0.9%
2111
0.4%
2091
0.4%
2081
0.4%

Avg Salary(K)
Real number (ℝ≥0)

HIGH CORRELATION

Distinct153
Distinct (%)65.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean98.15598291
Minimum15.5
Maximum225
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2022-10-02T10:05:28.086166image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum15.5
5-th percentile47.325
Q172.875
median95
Q3117.375
95-th percentile164.9
Maximum225
Range209.5
Interquartile range (IQR)44.5

Descriptive statistics

Standard deviation35.0578649
Coefficient of variation (CV)0.3571648295
Kurtosis0.3956477174
Mean98.15598291
Median Absolute Deviation (MAD)22.5
Skewness0.5724370744
Sum22968.5
Variance1229.053891
MonotonicityNot monotonic
2022-10-02T10:05:28.442244image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
87.55
 
2.1%
1214
 
1.7%
76.54
 
1.7%
98.54
 
1.7%
874
 
1.7%
1074
 
1.7%
1003
 
1.3%
80.53
 
1.3%
56.53
 
1.3%
813
 
1.3%
Other values (143)197
84.2%
ValueCountFrequency (%)
15.51
0.4%
27.51
0.4%
29.51
0.4%
37.51
0.4%
39.51
0.4%
431
0.4%
441
0.4%
44.52
0.9%
45.51
0.4%
472
0.9%
ValueCountFrequency (%)
2251
0.4%
2051
0.4%
1811
0.4%
1801
0.4%
1771
0.4%
1741
0.4%
1731
0.4%
171.51
0.4%
1691
0.4%
1682
0.9%

Job Location
Categorical

HIGH CORRELATION

Distinct32
Distinct (%)13.7%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
CA
58 
NY
23 
MA
23 
VA
21 
IL
13 
Other values (27)
96 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters468
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)3.0%

Sample

1st rowNM
2nd rowMD
3rd rowFL
4th rowWA
5th rowTX

Common Values

ValueCountFrequency (%)
CA58
24.8%
NY23
 
9.8%
MA23
 
9.8%
VA21
 
9.0%
IL13
 
5.6%
MD12
 
5.1%
PA10
 
4.3%
WA9
 
3.8%
TX8
 
3.4%
FL6
 
2.6%
Other values (22)51
21.8%

Length

2022-10-02T10:05:28.783413image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ca58
24.8%
ny23
 
9.8%
ma23
 
9.8%
va21
 
9.0%
il13
 
5.6%
md12
 
5.1%
pa10
 
4.3%
wa9
 
3.8%
tx8
 
3.4%
fl6
 
2.6%
Other values (22)51
21.8%

Most occurring characters

ValueCountFrequency (%)
A133
28.4%
C69
14.7%
M42
 
9.0%
N38
 
8.1%
Y23
 
4.9%
L23
 
4.9%
I22
 
4.7%
V21
 
4.5%
D18
 
3.8%
T14
 
3.0%
Other values (14)65
13.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter468
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A133
28.4%
C69
14.7%
M42
 
9.0%
N38
 
8.1%
Y23
 
4.9%
L23
 
4.9%
I22
 
4.7%
V21
 
4.5%
D18
 
3.8%
T14
 
3.0%
Other values (14)65
13.9%

Most occurring scripts

ValueCountFrequency (%)
Latin468
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A133
28.4%
C69
14.7%
M42
 
9.0%
N38
 
8.1%
Y23
 
4.9%
L23
 
4.9%
I22
 
4.7%
V21
 
4.5%
D18
 
3.8%
T14
 
3.0%
Other values (14)65
13.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII468
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A133
28.4%
C69
14.7%
M42
 
9.0%
N38
 
8.1%
Y23
 
4.9%
L23
 
4.9%
I22
 
4.7%
V21
 
4.5%
D18
 
3.8%
T14
 
3.0%
Other values (14)65
13.9%

Age
Real number (ℝ≥0)

HIGH CORRELATION

Distinct53
Distinct (%)22.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.27777778
Minimum2
Maximum60
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2022-10-02T10:05:29.092168image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile7
Q115
median22
Q336.75
95-th percentile55.35
Maximum60
Range58
Interquartile range (IQR)21.75

Descriptive statistics

Standard deviation15.06822032
Coefficient of variation (CV)0.5734206466
Kurtosis-0.5800323981
Mean26.27777778
Median Absolute Deviation (MAD)9
Skewness0.6866389576
Sum6149
Variance227.0512637
MonotonicityNot monotonic
2022-10-02T10:05:29.460291image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2125
 
10.7%
2515
 
6.4%
1113
 
5.6%
1513
 
5.6%
1312
 
5.1%
199
 
3.8%
567
 
3.0%
247
 
3.0%
396
 
2.6%
226
 
2.6%
Other values (43)121
51.7%
ValueCountFrequency (%)
21
 
0.4%
44
 
1.7%
52
 
0.9%
61
 
0.4%
75
 
2.1%
83
 
1.3%
95
 
2.1%
105
 
2.1%
1113
5.6%
123
 
1.3%
ValueCountFrequency (%)
604
1.7%
591
 
0.4%
567
3.0%
553
1.3%
542
 
0.9%
535
2.1%
525
2.1%
512
 
0.9%
501
 
0.4%
492
 
0.9%

Python
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
1
130 
0
104 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters234
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1130
55.6%
0104
44.4%

Length

2022-10-02T10:05:29.735831image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-02T10:05:29.938725image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1130
55.6%
0104
44.4%

Most occurring characters

ValueCountFrequency (%)
1130
55.6%
0104
44.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number234
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1130
55.6%
0104
44.4%

Most occurring scripts

ValueCountFrequency (%)
Common234
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1130
55.6%
0104
44.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII234
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1130
55.6%
0104
44.4%

spark
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
0
176 
1
58 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters234
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0176
75.2%
158
 
24.8%

Length

2022-10-02T10:05:30.112127image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-02T10:05:30.325337image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0176
75.2%
158
 
24.8%

Most occurring characters

ValueCountFrequency (%)
0176
75.2%
158
 
24.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number234
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0176
75.2%
158
 
24.8%

Most occurring scripts

ValueCountFrequency (%)
Common234
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0176
75.2%
158
 
24.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII234
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0176
75.2%
158
 
24.8%

aws
Categorical

Distinct2
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
0
180 
1
54 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters234
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0180
76.9%
154
 
23.1%

Length

2022-10-02T10:05:30.496711image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-02T10:05:30.722832image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0180
76.9%
154
 
23.1%

Most occurring characters

ValueCountFrequency (%)
0180
76.9%
154
 
23.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number234
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0180
76.9%
154
 
23.1%

Most occurring scripts

ValueCountFrequency (%)
Common234
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0180
76.9%
154
 
23.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII234
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0180
76.9%
154
 
23.1%

excel
Categorical

Distinct2
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
1
120 
0
114 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters234
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
1120
51.3%
0114
48.7%

Length

2022-10-02T10:05:30.903656image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-02T10:05:31.103199image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1120
51.3%
0114
48.7%

Most occurring characters

ValueCountFrequency (%)
1120
51.3%
0114
48.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number234
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1120
51.3%
0114
48.7%

Most occurring scripts

ValueCountFrequency (%)
Common234
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1120
51.3%
0114
48.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII234
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1120
51.3%
0114
48.7%

sql
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
1
139 
0
95 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters234
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
1139
59.4%
095
40.6%

Length

2022-10-02T10:05:31.295320image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-02T10:05:31.501002image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1139
59.4%
095
40.6%

Most occurring characters

ValueCountFrequency (%)
1139
59.4%
095
40.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number234
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1139
59.4%
095
40.6%

Most occurring scripts

ValueCountFrequency (%)
Common234
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1139
59.4%
095
40.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII234
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1139
59.4%
095
40.6%

sas
Categorical

Distinct2
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
0
210 
1
24 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters234
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0210
89.7%
124
 
10.3%

Length

2022-10-02T10:05:31.675132image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-02T10:05:31.882063image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0210
89.7%
124
 
10.3%

Most occurring characters

ValueCountFrequency (%)
0210
89.7%
124
 
10.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number234
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0210
89.7%
124
 
10.3%

Most occurring scripts

ValueCountFrequency (%)
Common234
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0210
89.7%
124
 
10.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII234
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0210
89.7%
124
 
10.3%

keras
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
0
226 
1
 
8

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters234
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0226
96.6%
18
 
3.4%

Length

2022-10-02T10:05:32.094622image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-02T10:05:32.299574image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0226
96.6%
18
 
3.4%

Most occurring characters

ValueCountFrequency (%)
0226
96.6%
18
 
3.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number234
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0226
96.6%
18
 
3.4%

Most occurring scripts

ValueCountFrequency (%)
Common234
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0226
96.6%
18
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII234
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0226
96.6%
18
 
3.4%

pytorch
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
0
220 
1
 
14

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters234
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0220
94.0%
114
 
6.0%

Length

2022-10-02T10:05:32.483749image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-02T10:05:32.673531image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0220
94.0%
114
 
6.0%

Most occurring characters

ValueCountFrequency (%)
0220
94.0%
114
 
6.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number234
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0220
94.0%
114
 
6.0%

Most occurring scripts

ValueCountFrequency (%)
Common234
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0220
94.0%
114
 
6.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII234
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0220
94.0%
114
 
6.0%

scikit
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
0
214 
1
 
20

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters234
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0214
91.5%
120
 
8.5%

Length

2022-10-02T10:05:32.846668image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-02T10:05:33.051364image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0214
91.5%
120
 
8.5%

Most occurring characters

ValueCountFrequency (%)
0214
91.5%
120
 
8.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number234
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0214
91.5%
120
 
8.5%

Most occurring scripts

ValueCountFrequency (%)
Common234
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0214
91.5%
120
 
8.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII234
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0214
91.5%
120
 
8.5%

tensor
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
0
210 
1
24 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters234
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0210
89.7%
124
 
10.3%

Length

2022-10-02T10:05:33.585639image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-02T10:05:33.789133image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0210
89.7%
124
 
10.3%

Most occurring characters

ValueCountFrequency (%)
0210
89.7%
124
 
10.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number234
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0210
89.7%
124
 
10.3%

Most occurring scripts

ValueCountFrequency (%)
Common234
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0210
89.7%
124
 
10.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII234
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0210
89.7%
124
 
10.3%

hadoop
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
0
187 
1
47 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters234
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0187
79.9%
147
 
20.1%

Length

2022-10-02T10:05:33.961639image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-02T10:05:34.168173image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0187
79.9%
147
 
20.1%

Most occurring characters

ValueCountFrequency (%)
0187
79.9%
147
 
20.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number234
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0187
79.9%
147
 
20.1%

Most occurring scripts

ValueCountFrequency (%)
Common234
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0187
79.9%
147
 
20.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII234
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0187
79.9%
147
 
20.1%

tableau
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
0
183 
1
51 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters234
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0183
78.2%
151
 
21.8%

Length

2022-10-02T10:05:34.353114image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-02T10:05:34.562579image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0183
78.2%
151
 
21.8%

Most occurring characters

ValueCountFrequency (%)
0183
78.2%
151
 
21.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number234
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0183
78.2%
151
 
21.8%

Most occurring scripts

ValueCountFrequency (%)
Common234
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0183
78.2%
151
 
21.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII234
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0183
78.2%
151
 
21.8%

bi
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
0
213 
1
 
21

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters234
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0213
91.0%
121
 
9.0%

Length

2022-10-02T10:05:34.741769image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-02T10:05:34.946142image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0213
91.0%
121
 
9.0%

Most occurring characters

ValueCountFrequency (%)
0213
91.0%
121
 
9.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number234
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0213
91.0%
121
 
9.0%

Most occurring scripts

ValueCountFrequency (%)
Common234
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0213
91.0%
121
 
9.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII234
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0213
91.0%
121
 
9.0%

flink
Categorical

Distinct2
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
0
229 
1
 
5

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters234
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0229
97.9%
15
 
2.1%

Length

2022-10-02T10:05:35.217234image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-02T10:05:35.484928image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0229
97.9%
15
 
2.1%

Most occurring characters

ValueCountFrequency (%)
0229
97.9%
15
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number234
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0229
97.9%
15
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
Common234
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0229
97.9%
15
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII234
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0229
97.9%
15
 
2.1%

mongo
Categorical

Distinct2
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
0
221 
1
 
13

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters234
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0221
94.4%
113
 
5.6%

Length

2022-10-02T10:05:35.651600image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-02T10:05:35.984212image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0221
94.4%
113
 
5.6%

Most occurring characters

ValueCountFrequency (%)
0221
94.4%
113
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number234
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0221
94.4%
113
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
Common234
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0221
94.4%
113
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII234
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0221
94.4%
113
 
5.6%

google_an
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
0
227 
1
 
7

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters234
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0227
97.0%
17
 
3.0%

Length

2022-10-02T10:05:36.159665image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-02T10:05:36.364819image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0227
97.0%
17
 
3.0%

Most occurring characters

ValueCountFrequency (%)
0227
97.0%
17
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number234
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0227
97.0%
17
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
Common234
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0227
97.0%
17
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII234
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0227
97.0%
17
 
3.0%

job_title_sim
Categorical

HIGH CORRELATION

Distinct9
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
data scientist
103 
analyst
42 
data engineer
41 
other scientist
33 
Data scientist project manager
 
4
Other values (4)
11 

Length

Max length30
Median length25
Mean length12.90598291
Min length2

Characters and Unicode

Total characters3020
Distinct characters19
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowdata scientist
2nd rowdata scientist
3rd rowdata scientist
4th rowdata scientist
5th rowdata scientist

Common Values

ValueCountFrequency (%)
data scientist103
44.0%
analyst42
17.9%
data engineer41
 
17.5%
other scientist33
 
14.1%
Data scientist project manager4
 
1.7%
data analitics4
 
1.7%
na3
 
1.3%
data modeler2
 
0.9%
machine learning engineer2
 
0.9%

Length

2022-10-02T10:05:36.549598image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-02T10:05:36.834829image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
data154
35.6%
scientist140
32.3%
engineer43
 
9.9%
analyst42
 
9.7%
other33
 
7.6%
project4
 
0.9%
manager4
 
0.9%
analitics4
 
0.9%
na3
 
0.7%
modeler2
 
0.5%
Other values (2)4
 
0.9%

Most occurring characters

ValueCountFrequency (%)
t517
17.1%
a415
13.7%
i335
11.1%
s326
10.8%
e318
10.5%
n285
9.4%
199
 
6.6%
d152
 
5.0%
c150
 
5.0%
r88
 
2.9%
Other values (9)235
7.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2817
93.3%
Space Separator199
 
6.6%
Uppercase Letter4
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t517
18.4%
a415
14.7%
i335
11.9%
s326
11.6%
e318
11.3%
n285
10.1%
d152
 
5.4%
c150
 
5.3%
r88
 
3.1%
l50
 
1.8%
Other values (7)181
 
6.4%
Space Separator
ValueCountFrequency (%)
199
100.0%
Uppercase Letter
ValueCountFrequency (%)
D4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2821
93.4%
Common199
 
6.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
t517
18.3%
a415
14.7%
i335
11.9%
s326
11.6%
e318
11.3%
n285
10.1%
d152
 
5.4%
c150
 
5.3%
r88
 
3.1%
l50
 
1.8%
Other values (8)185
 
6.6%
Common
ValueCountFrequency (%)
199
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII3020
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t517
17.1%
a415
13.7%
i335
11.1%
s326
10.8%
e318
10.5%
n285
9.4%
199
 
6.6%
d152
 
5.0%
c150
 
5.0%
r88
 
2.9%
Other values (9)235
7.8%

seniority_by_title
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
na
170 
sr
63 
jr
 
1

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters468
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.4%

Sample

1st rowna
2nd rowna
3rd rowna
4th rowna
5th rowna

Common Values

ValueCountFrequency (%)
na170
72.6%
sr63
 
26.9%
jr1
 
0.4%

Length

2022-10-02T10:05:37.140759image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-02T10:05:37.366821image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
na170
72.6%
sr63
 
26.9%
jr1
 
0.4%

Most occurring characters

ValueCountFrequency (%)
n170
36.3%
a170
36.3%
r64
 
13.7%
s63
 
13.5%
j1
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter468
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n170
36.3%
a170
36.3%
r64
 
13.7%
s63
 
13.5%
j1
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Latin468
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n170
36.3%
a170
36.3%
r64
 
13.7%
s63
 
13.5%
j1
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII468
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n170
36.3%
a170
36.3%
r64
 
13.7%
s63
 
13.5%
j1
 
0.2%

Degree
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
na
131 
M
76 
P
27 

Length

Max length2
Median length2
Mean length1.55982906
Min length1

Characters and Unicode

Total characters365
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowM
2nd rowM
3rd rowM
4th rowna
5th rowna

Common Values

ValueCountFrequency (%)
na131
56.0%
M76
32.5%
P27
 
11.5%

Length

2022-10-02T10:05:37.606659image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-02T10:05:37.850170image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
na131
56.0%
m76
32.5%
p27
 
11.5%

Most occurring characters

ValueCountFrequency (%)
n131
35.9%
a131
35.9%
M76
20.8%
P27
 
7.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter262
71.8%
Uppercase Letter103
 
28.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n131
50.0%
a131
50.0%
Uppercase Letter
ValueCountFrequency (%)
M76
73.8%
P27
 
26.2%

Most occurring scripts

ValueCountFrequency (%)
Latin365
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n131
35.9%
a131
35.9%
M76
20.8%
P27
 
7.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII365
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n131
35.9%
a131
35.9%
M76
20.8%
P27
 
7.4%

Min_Salary
Real number (ℝ≥0)

HIGH CORRELATION

Distinct94
Distinct (%)40.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean71.31196581
Minimum10
Maximum200
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2022-10-02T10:05:38.116937image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile31
Q151.25
median68
Q386
95-th percentile118.7
Maximum200
Range190
Interquartile range (IQR)34.75

Descriptive statistics

Standard deviation28.85305575
Coefficient of variation (CV)0.4046032867
Kurtosis1.867899292
Mean71.31196581
Median Absolute Deviation (MAD)18
Skewness0.8536435912
Sum16687
Variance832.4988262
MonotonicityNot monotonic
2022-10-02T10:05:38.440159image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
527
 
3.0%
836
 
2.6%
806
 
2.6%
606
 
2.6%
646
 
2.6%
746
 
2.6%
865
 
2.1%
655
 
2.1%
635
 
2.1%
425
 
2.1%
Other values (84)177
75.6%
ValueCountFrequency (%)
101
 
0.4%
151
 
0.4%
181
 
0.4%
201
 
0.4%
211
 
0.4%
241
 
0.4%
272
0.9%
291
 
0.4%
314
1.7%
323
1.3%
ValueCountFrequency (%)
2001
0.4%
1901
0.4%
1391
0.4%
1381
0.4%
1321
0.4%
1311
0.4%
1301
0.4%
1271
0.4%
1262
0.9%
1211
0.4%

Max_Salary
Real number (ℝ≥0)

HIGH CORRELATION

Distinct123
Distinct (%)52.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean123.8333333
Minimum16
Maximum250
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2022-10-02T10:05:38.792532image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum16
5-th percentile58.3
Q195
median120.5
Q3148
95-th percentile207.35
Maximum250
Range234
Interquartile range (IQR)53

Descriptive statistics

Standard deviation43.90638207
Coefficient of variation (CV)0.3545602859
Kurtosis0.09923200825
Mean123.8333333
Median Absolute Deviation (MAD)27.5
Skewness0.3048904112
Sum28977
Variance1927.770386
MonotonicityNot monotonic
2022-10-02T10:05:39.095012image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1137
 
3.0%
1195
 
2.1%
1335
 
2.1%
855
 
2.1%
1275
 
2.1%
1375
 
2.1%
1124
 
1.7%
1294
 
1.7%
1404
 
1.7%
1424
 
1.7%
Other values (113)186
79.5%
ValueCountFrequency (%)
161
0.4%
171
0.4%
251
0.4%
291
0.4%
392
0.9%
471
0.4%
481
0.4%
501
0.4%
551
0.4%
572
0.9%
ValueCountFrequency (%)
2501
0.4%
2311
0.4%
2282
0.9%
2241
0.4%
2231
0.4%
2211
0.4%
2202
0.9%
2111
0.4%
2091
0.4%
2081
0.4%

Min_Revenue
Real number (ℝ≥0)

HIGH CORRELATION

Distinct10
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1337752137
Minimum1000000
Maximum1 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2022-10-02T10:05:39.349033image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1000000
5-th percentile10000000
Q150000000
median100000000
Q31000000000
95-th percentile1 × 1010
Maximum1 × 1010
Range9999000000
Interquartile range (IQR)950000000

Descriptive statistics

Standard deviation2798745298
Coefficient of variation (CV)2.092125455
Kurtosis5.135877727
Mean1337752137
Median Absolute Deviation (MAD)90000000
Skewness2.555829799
Sum3.13034 × 1011
Variance7.832975243 × 1018
MonotonicityNot monotonic
2022-10-02T10:05:39.552646image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
10000000048
20.5%
5000000040
17.1%
50000000025
10.7%
100000000024
10.3%
200000000022
9.4%
2500000021
9.0%
1 × 101020
8.5%
1000000020
8.5%
10000009
 
3.8%
50000000005
 
2.1%
ValueCountFrequency (%)
10000009
 
3.8%
1000000020
8.5%
2500000021
9.0%
5000000040
17.1%
10000000048
20.5%
50000000025
10.7%
100000000024
10.3%
200000000022
9.4%
50000000005
 
2.1%
1 × 101020
8.5%
ValueCountFrequency (%)
1 × 101020
8.5%
50000000005
 
2.1%
200000000022
9.4%
100000000024
10.3%
50000000025
10.7%
10000000048
20.5%
5000000040
17.1%
2500000021
9.0%
1000000020
8.5%
10000009
 
3.8%

Max_Revenue
Real number (ℝ≥0)

HIGH CORRELATION

Distinct11
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1971833333
Minimum1000000
Maximum1 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2022-10-02T10:05:39.789994image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1000000
5-th percentile10000000
Q150000000
median500000000
Q32000000000
95-th percentile1 × 1010
Maximum1 × 1010
Range9999000000
Interquartile range (IQR)1950000000

Descriptive statistics

Standard deviation3123711602
Coefficient of variation (CV)1.584166141
Kurtosis1.918435744
Mean1971833333
Median Absolute Deviation (MAD)490000000
Skewness1.807959976
Sum4.61409 × 1011
Variance9.757574174 × 1018
MonotonicityNot monotonic
2022-10-02T10:05:40.000692image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
50000000048
20.5%
10000000027
11.5%
100000000025
10.7%
1 × 101025
10.7%
200000000024
10.3%
500000000022
9.4%
5000000021
9.0%
2500000020
8.5%
1000000013
 
5.6%
50000005
 
2.1%
ValueCountFrequency (%)
10000004
 
1.7%
50000005
 
2.1%
1000000013
 
5.6%
2500000020
8.5%
5000000021
9.0%
10000000027
11.5%
50000000048
20.5%
100000000025
10.7%
200000000024
10.3%
500000000022
9.4%
ValueCountFrequency (%)
1 × 101025
10.7%
500000000022
9.4%
200000000024
10.3%
100000000025
10.7%
50000000048
20.5%
10000000027
11.5%
5000000021
9.0%
2500000020
8.5%
1000000013
 
5.6%
50000005
 
2.1%

Interactions

2022-10-02T10:05:16.707693image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T10:04:52.526163image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T10:04:55.414495image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T10:04:58.045483image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T10:05:00.617665image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T10:05:03.355276image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T10:05:05.724653image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T10:05:08.749441image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T10:05:11.844874image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T10:05:14.259827image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T10:05:16.941049image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T10:04:52.811687image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T10:04:55.664342image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T10:04:58.284038image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T10:05:00.899325image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T10:05:03.582215image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T10:05:06.024267image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T10:05:09.641186image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T10:05:12.075741image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T10:05:14.502487image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T10:05:17.198703image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T10:04:53.100535image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T10:04:55.985761image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T10:04:58.586469image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T10:05:01.225770image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T10:05:03.818821image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T10:05:06.358585image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T10:05:09.898239image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T10:05:12.312402image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T10:05:14.786227image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T10:05:17.451970image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T10:04:53.432532image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T10:04:56.255957image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T10:04:58.854985image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T10:05:01.496009image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T10:05:04.082690image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T10:05:06.613708image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T10:05:10.160410image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T10:05:12.547390image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T10:05:15.074531image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T10:05:17.675024image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T10:04:53.755662image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T10:04:56.501845image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T10:04:59.084136image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T10:05:01.815376image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T10:05:04.325546image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T10:05:06.915688image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T10:05:10.378120image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T10:05:12.778350image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T10:05:15.289739image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T10:05:17.893001image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T10:04:53.974910image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T10:04:56.758113image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T10:04:59.298709image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T10:05:02.053081image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T10:05:04.530381image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T10:05:07.221721image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T10:05:10.613045image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T10:05:13.023035image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T10:05:15.498863image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T10:05:18.132975image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T10:04:54.211419image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T10:04:57.014637image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T10:04:59.552490image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T10:05:02.301918image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T10:05:04.755388image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T10:05:07.487948image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T10:05:10.880845image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T10:05:13.306022image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T10:05:15.723511image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T10:05:18.375697image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T10:04:54.465510image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T10:04:57.288932image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T10:04:59.789683image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T10:05:02.554270image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T10:05:05.006508image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T10:05:07.923564image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T10:05:11.147167image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T10:05:13.584247image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T10:05:15.977488image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T10:05:18.624961image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T10:04:54.760934image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T10:04:57.525335image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T10:05:00.051697image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T10:05:02.779870image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T10:05:05.271476image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T10:05:08.183079image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T10:05:11.371092image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T10:05:13.796673image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T10:05:16.210196image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T10:05:18.845486image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T10:04:55.099595image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T10:04:57.791502image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T10:05:00.324451image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T10:05:03.039891image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T10:05:05.498881image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T10:05:08.456201image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T10:05:11.617688image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T10:05:14.037148image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-02T10:05:16.466637image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-10-02T10:05:40.286081image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-10-02T10:05:40.906903image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-10-02T10:05:41.526869image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-10-02T10:05:42.131077image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-10-02T10:05:42.735137image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-10-02T10:05:19.406536image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-10-02T10:05:21.980272image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

Job TitleRatingSizeFoundedType of ownershipIndustrySectorHourlyEmployer providedLower SalaryUpper SalaryAvg Salary(K)Job LocationAgePythonsparkawsexcelsqlsaskeraspytorchscikittensorhadooptableaubiflinkmongogoogle_anjob_title_simseniority_by_titleDegreeMin_SalaryMax_SalaryMin_RevenueMax_Revenue
0Data Scientist3.8501 - 10001973Company - PrivateAerospace & DefenseAerospace & Defense00539172.0NM481001010000011000data scientistnaM539150000000100000000
1Healthcare Data Scientist3.410000+1984Other OrganizationHealth Care Services & HospitalsHealth Care006311287.5MD371000000000000000data scientistnaM6311220000000005000000000
2Data Scientist4.8501 - 10002010Company - PrivateSecurity ServicesBusiness Services00809085.0FL111101110000000000data scientistnaM8090100000000500000000
3Data Scientist3.81001 - 50001965GovernmentEnergyOil, Gas, Energy & Utilities00569776.5WA561000000000000000data scientistnana56975000000001000000000
4Data Scientist3.4201 - 5002000Company - PublicReal EstateReal Estate007111995.0TX211011100000001010data scientistnana7111910000000002000000000
5Data Scientist3.8201 - 5002005Company - PrivateConsultingBusiness Services0086142114.0CA161111100101000000data scientistnaM861422500000050000000
6Research Scientist3.310000+2014HospitalHealth Care Services & HospitalsHealth Care00388461.0NY70000000000000000other scientistnaP38845000000001000000000
7Data Scientist4.651 - 2002009Company - PrivateInternetInformation Technology00120160140.0NY121100000000000000data scientistnana120160100000000500000000
8Data Scientist3.5501 - 10002011Company - PrivateOther Retail StoresRetail00126201163.5CA101000000000000000data scientistnana12620110000000002000000000
9Data Scientist4.15001 - 100001968Company - PublicResearch & DevelopmentBusiness Services006410685.0VA530000100000100000data scientistnana6410610000000002000000000

Last rows

Job TitleRatingSizeFoundedType of ownershipIndustrySectorHourlyEmployer providedLower SalaryUpper SalaryAvg Salary(K)Job LocationAgePythonsparkawsexcelsqlsaskeraspytorchscikittensorhadooptableaubiflinkmongogoogle_anjob_title_simseniority_by_titleDegreeMin_SalaryMax_SalaryMin_RevenueMax_Revenue
224Quality Control Scientist III- Analytical Development2.7201 - 5001961Company - PrivateBiotech & PharmaceuticalsBiotech & Pharmaceuticals004811380.5MD600001000000000000other scientistnana481132500000050000000
225Clinical Scientist, Clinical Development3.851 - 2002008Company - PrivateBiotech & PharmaceuticalsBiotech & Pharmaceuticals10569776.5MA130001000000000000other scientistnana27471000000025000000
226Manager, Safety Scientist, Medical Safety & Risk Management3.8501 - 10002008Company - PublicBiotech & PharmaceuticalsBiotech & Pharmaceuticals006812596.5MA130001000000000000Data scientist project managernana6812550000000100000000
227Data Scientist3.51 - 50-1School / School DistrictK-12 EducationEducation007112196.0MO210001100000100000data scientistnaP711215000000010000000
228Data Scientist3.451 - 2001997Company - PrivateConsultingBusiness Services007212196.5DC241000000001000000data scientistnaM721211000000025000000
229Data Scientist4.3201 - 5001969Company - PrivateIT ServicesInformation Technology00518869.5AZ521001110010000000data scientistnana518850000000100000000
230Data Scientist (Warehouse Automation)3.8201 - 5002005Subsidiary or Business SegmentConsumer Products ManufacturingManufacturing0079127103.0CA161010100000011000data scientistnaM791272500000050000000
231Data Architect / Data Modeler4.31001 - 50001999Company - PublicEnterprise Software & Network SolutionsInformation Technology006311086.5NY220011100000000000data modelernaM631105000000001000000000
232Data Scientist3.451 - 200-1Company - PrivateIT ServicesInformation Technology006511389.0WA210000000000000000data scientistnana651135000000010000000
233Data Engineer3.9201 - 5002011Company - PrivateInternetInformation Technology006211387.5CA101011100000000000data engineernaP62113100000000500000000